At a Glance
- Tasks: Join a small team to build and maintain high-quality data for predictive modelling.
- Company: Established tech company in the exciting sports/gaming sector.
- Benefits: Hybrid work model, private health insurance, enhanced parental pay, and generous holiday allowance.
- Other info: Dynamic environment with opportunities for continuous improvement and career growth.
- Why this job: Make an impact by transforming messy datasets into reliable assets for innovative projects.
- Qualifications: Strong experience in data engineering, Python, and SQL with complex datasets.
The predicted salary is between 63000 - 77000 £ per year.
Quant Data Engineer | London (Hybrid) | Up to £90K
We're working with an established technology company in the sports/gaming sector, building predictive models powered by a wide and growing range of data inputs.
It's intricate, technically demanding work.
They're looking for a Quant Data Engineer to join a small team working directly alongside quantitative modellers, ensuring they have reliable, well-structured, high-quality data for research and modelling.
Strong focus on data quality, investigation and continuous improvement, turning complex, messy datasets into assets the business can depend on.
- What you'll be doing
- Modelling Data Support
- Prepare and maintain research/modelling datasets, and investigate issues affecting model outputs
- Improve data structure, usability and quality across analytical workflows
- Assess new and existing data sources for viability and integration
- Data Engineering
- Build and maintain Python-based data pipelines (ingestion, transformation, validation)
- Maintain historical data assets and support migrations/backfills
- Collaborate with engineers on upstream/downstream data flow improvements
- Data Quality & Investigation
- Own validation, reconciliation and monitoring across key datasets
- Document data logic, transformations and assumptions; improve metadata and standardisation
- Resolve anomalies and gaps with engineering and operations
- What you'll need
- Strong experience as a Data Engineer, Research Data Engineer, or similar, working with complex datasets
- Strong Python for data processing, investigation and workflow development
- Excellent SQL and relational database experience, Postgre SQL preferred
- Track record preparing, transforming and validating datasets for analytical/research use
- Strong experience tracing data issues through pipelines and source systems
- Comfortable working with messy, incomplete or evolving datasets
- Nice to have: Click House, Big Query, Snowflake, Redshift or similar
Benefits
Based in London, working a hybrid model (office time required a couple of days a week).
Benefits include
- private health & dental insurance
- cycle to work scheme
- enhanced parental and sick pay
- above-standard holiday allowance
- Quant Data Engineer | London (Hybrid) | Up to £90K
Burns Sheehan Ltd will consider applications based only on skills and ability and will not discriminate on any grounds.
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Quant Data Engineer employer: Burns Sheehan
Burns Sheehan offers an exceptional work environment in Oxford, where innovation meets collaboration. As a Platform Engineer, you will not only tackle unique engineering challenges but also benefit from a culture that prioritises employee growth and development. With access to cutting-edge technology and a commitment to making a visible impact in the robotics field, this role provides a rewarding opportunity for those looking to thrive in a high-growth technology landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Quant Data Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Burns Sheehan!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Quant Data Engineer at Burns Sheehan.
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Burns Sheehan.
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We think you need these skills to ace Quant Data Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Burns Sheehan, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Burns Sheehan. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Burns Sheehan
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Burns Sheehan!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
